Download Classification-Based Data Mining in Target Marketing

Volume 6, Issue 3, March 2016
ISSN: 2277 128X
International Journal of Advanced Research in
Computer Science and Software Engineering
Research Paper
Available online at: www.ijarcsse.com
Classification-Based Data Mining in Target Marketing
N. Mlambo
Lecturer, School of Information and Communication Technology, College of Science and Technology,
University of Rwanda, Rwanda
Abstract: Target marketing is the selection of potential customers to whom a business wishes to sellproducts or
services. The targeting strategy involves segmenting the market, choosing which segments of the market are
appropriate, and determining the products that will be offered in each segment. A business offering multiple products
can determine if the various segments should receive one generic product (such as in mass marketing), or if each
segment should receive a customized product (multi-segment), based upon the market'sdiversity, maturity, the level of
competition and the volume of sales expected. In this paper we look at the applications of classification-based data
mining techniques in target marketing and some specific models that were developed to assist in the same.
Keywords: Data mining, target marketing, classification, direct marketing
I. INTRODUCTION
Data mining refers to the extraction of hidden predictive information from large data sources, mainly data
warehouses. The target of data mining [1, 3] is to find unexpected characteristics, hidden features or other unclear
relationships inside the data based on techniques combinations.There are two major tasks of data mining namely;
predictive and descriptive. Predictive mining tasks perform inference on the current data in order to make predictions.
Descriptive mining tasks characterize the general properties of the data in the data source. Several major Data Mining
techniques have been developed and used in data mining projects recently including clustering, association, prediction,
sequential patterns, classification, etc [2]
II. CLASSIFICATION
Classification is a very popular data mining technique, which employs a set of pre-classified examples to develop a
model that can classify the population of records. It can be used to predict categorical class labels and classifies data on
training set and class labels and hence can be used for classifying newly available data [2, 5, 12]. There are many
classification techniques, which include; neural networks, decision trees, Bayesian networks, k-nearest neighbor, and
support vector machines. Fraud detection and credit –risk applications are particularly well suited to this type of analysis.
III. APPLICATION OF CLASSIFICATION IN DIRECT MARKETING
Almost all industries that sell products and servicesneed to advertise and promote their products and services.There are
generally two approachesto advertisement and promotion: mass marketing anddirect marketing [9]. Mass marketing,
which uses massmedia such as television, radio, and newspapers, broadcastsmessages to the public without
discrimination. Itused to be an effective way of promotion when the productswere in great demand by the public.
Direct marketing is a process of identifying likely buyersof certain products and promoting the products accordingly.
Instead of promoting to customers indiscriminatively, this marketing technique studies customers’ characteristics and
needs, and selects certain customers as the target for promotion [8, 9]. The hope is that the responserate for the selected
customers can be much improved. Direct marketing has generated an increasing interest among academics and
practitioners over the past few years mainly due to competitive market environment, advancement in technology and
changing behavior of customers which are difficult to predict [8]. It is increasingly used by banks, insurance companies,
and the retail industry.
3.1 Life Insurance Companies
Modeling underwriting decisions helps in selling life insurance by identifying potential customers who are more
likely to qualify for life insurance products. Marketing expenses are significant portions of life insurance company
budgets, and utilizing them efficiently is a key operational strategy.
3.2 Retail Industry
Classification can help direct marketers by providing useful and accurate trends on purchasing behavior of their
customers and also help them in predicting which products their customers may be interested in buying. In fact data
mining allows retail store managers to identify their best customers, attract customers and inform customers via mail
marketing, and maximize profitability by means of identifying profitable customers.
© 2016, IJARCSSE All Rights Reserved
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Mlambo International Journal of Advanced Research in Computer Science and Software Engineering 6(3),
March - 2016, pp. 29-31
3.3 Financial, banking and credit scoring
Classification can assist financial institutions in various ways, such as credit reporting, credit rating, loan or credit
card approval by predicting good customers, mode of service delivery and customer retention (i.e build profiles of
customers likely to use which services. A credit card company can leverage its vast warehouse of customer transaction
data to identify customers most likely to be interested in a new credit product and target the same.
IV.
EXAMPLES OF CLASSIFICATION-BASED MODELS DEVELOPED TO ASSIST DIRECT
MARKETING
4.1 Bank Direct Marketing Based on Neural Network [7]
Data mining has gained popularity for illustrative and predictive applications in banking processes. In this
application the decision tree model (C5.0), and the Multilayer perceptron neural network (MLPNN) with back
propagation are applied on the bank direct marketing. The C5.0 classifier is selected because it gives more accurate and
efficient output with comparatively high speed. Memory usage to store the rule set in case of the C5.0 classifier is less as
it generates smaller decision trees [10]. The dataset well known as bank marketing from the University of California at
Irvine (UCI) [11] is used.
The objective is to examine the performance of MLPNN and C5.0 models on a real-world data of bank deposit
subscription. The purpose is increasing the campaign effectiveness by identifying the main characteristics that affect a
success (the deposit subscribed by the client) based on MLPNN and C5.0. The experimental results demonstrate, with
higher accuracies, the success of these models in predicting the best campaign contact with the clients for subscribing
deposit. The performances are measured by three statistical measures; classification accuracy, sensitivity, and specificity.
4.2 A Data Mining-Based Response Model for Target Selection in Direct Marketing [12] (Naïve Bayes Algorithm)
Using historical purchase data, a predictive response model with data mining techniques was developed to predict a
probability that a customer in Ebedi Microfinance bank (Nigeria) will respond to a promotion or an offer. To achieve this
purpose, a predictive response model using customers’ historical purchase data was built with data mining techniques.
The data were stored in a data warehouse to serve as management decision support system. The response model was built
from customers’ historic purchases and demographic dataset.
The purchase behaviour variables used in the model development are as follow.
Recency: This is the number of months since the last purchase and first purchase. It is typically the most powerful of the
three characteristics for predicting response to a subsequent offer. This seems quite logical. It says that if you have
recently purchased something from a company, you are more likely to make another purchase than someone who did not
recently make a purchase.
Frequency: This is the number of purchases. It can be the total of purchases within a specific time frame or include all
purchases. This characteristic is second to recency in predictive power for response. Again, it is quite intuitive as to why
it relates to future purchases.
Monetary value: This is the total amount. Similar to frequency, it can be within a specific time frame or include all
purchases. Of the three, this characteristic is the least powerful when it comes to predicting response. But when used in
combination, it can add another dimension of understanding.
Demographic information includes customers’ personal characteristics and information such as age, sex, address,
profession etc
Bayesian algorithm precisely Naïve Bayes algorithm was employed in constructing the classifier system. Both filter
and wrapper feature selection techniques were employed in determining inputs to the model.
The results obtained shows that Ebedi Microfinance bank can plan effective marketing of their products and services
by obtaining a guiding report on the status of their customers which will go a long way in assisting management in
saving significant amount of money that could have been spent on wasteful promotional campaigns.
V. CONCLUSION
A target marketing model helps to establish aguideline of marketing strategies. Marketing strategy for a bank should
target those valuable consumers andmaintain a good relationship in order to generate greater revenue. The bank can
position the target consumersprecisely and implement various marketing strategies to satisfy consumers’ needs. The
same should be done in companies that are in retail and life insurance industries, etc. Classification-based techniques
come in handy and this explains why data mining is becoming very popular.
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Mlambo International Journal of Advanced Research in Computer Science and Software Engineering 6(3),
March - 2016, pp. 29-31
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